Starburst models of merging galaxies

In the past decade, infrared observations have shown that interacting and merging galaxies have higher luminosities than isolated systems, with the luminosities in mergers as high as 10(exp 12) solar luminosity. However, the origin of the luminosity found in mergers is controversial, with two main competing theories. The first is the starburst scenario. As two gas rich galaxies start to merge, cloud-cloud collisions induce fast shocks in the molecular gas. This gas cools, collapses, and fragments, producing a blast of star formation. The main rival to this theory is that the infrared luminosity is produced by a dust embedded active nucleus, the merger of two gas rich galaxies providing the 'fuel to feed the monster'. There has even been speculation that there is an evolutionary link between starbursts and active nuclei, and that possibly active galactic nuclei (AGN's) and QSO's were formed from a starburst. Assuming that the infrared luminosity in merging galaxies is due to star formation, there should be ionizing photons produced from the high mass stars, giving rise to recombination line emission. The objective is to use a simple starburst model to test the hypothesis that the extreme infrared luminosity of merging galaxies is due to a starburst

Can implementation intentions and text messages promote brisk walking? A randomized trial.

Objective: To test the efficacy in promoting brisk walking of two theory-based interventions that incorporate implementation intentions and text message (Short Message Service; SMS) reminders directed at one’s walking-related plans or goals. Design: Participants (N = 149) were randomized to one of three conditions (implementation intention + SMS plan reminder, implementation intention + SMS goal reminder, control) before completing measures at baseline and follow-up 4 weeks later. At follow-up, the experimental groups were given a surprise recall task concerning their plans. All participants completed an equivalent goal recall task. Main Outcome Measures: Validated self-report measures of physical activity and measures of implementation intention and goal recall, weight, and waist-to-hip ratio. Results: Both intervention groups increased their brisk walking relative to the control group, without reducing other physical activity. The goal reminder group lost the most weight. The SMS plan reminder group recalled more of their plans than the SMS goal reminder group, but the latter were more successful in goal recall. Conclusion: Both interventions can promote brisk walking in sedentary populations. Text messages aid the recall of, and could enhance interventions that target, implementation intentions and goals

Boosting Haplotype Inference with Local Search

Abstract. A very challenging problem in the genetics domain is to infer haplotypes from genotypes. This process is expected to identify genes affecting health, disease and response to drugs. One of the approaches to haplotype inference aims to minimise the number of different haplotypes used, and is known as haplotype inference by pure parsimony (HIPP). The HIPP problem is computationally difficult, being NP-hard. Recently, a SAT-based method (SHIPs) has been proposed to solve the HIPP problem. This method iteratively considers an increasing number of haplotypes, starting from an initial lower bound. Hence, one important aspect of SHIPs is the lower bounding procedure, which reduces the number of iterations of the basic algorithm, and also indirectly simplifies the resulting SAT model. This paper describes the use of local search to improve existing lower bounding procedures. The new lower bounding procedure is guaranteed to be as tight as the existing procedures. In practice the new procedure is in most cases considerably tighter, allowing significant improvement of performance on challenging problem instances.

A Grouping Genetic Algorithm for Joint Stratification and Sample Allocation Designs

Predicting the cheapest sample size for the optimal stratification in multivariate survey design is a problem in cases where the population frame is large. A solution exists that iteratively searches for the minimum sample size necessary to meet accuracy constraints in partitions of atomic strata created by the Cartesian product of auxiliary variables into larger strata. The optimal stratification can be found by testing all possible partitions. However the number of possible partitions grows exponentially with the number of initial strata. There are alternative ways of modelling this problem, one of the most natural is using Genetic Algorithms (GA). These evolutionary algorithms use recombination, mutation and selection to search for optimal solutions. They often converge on optimal or near-optimal solution more quickly than exact methods. We propose a new GA approach to this problem using grouping genetic operators instead of traditional operators. The results show a significant improvement in solution quality for similar computational effort, corresponding to large monetary savings.Comment: 22 page